Risk-Averse Optimal Bidding Strategy for Demand-Side Resource Aggregators in Day-Ahead Electricity Markets Under Uncertainty

2017 ◽  
Vol 8 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Zhiwei Xu ◽  
Zechun Hu ◽  
Yonghua Song ◽  
Jianhui Wang
Author(s):  
Evaggelos G. Kardakos ◽  
Christos K. Simoglou ◽  
Stylianos I. Vagropoulos ◽  
Anastasios G. Bakirtzis

2014 ◽  
Vol 521 ◽  
pp. 476-479 ◽  
Author(s):  
Guo Zhong Liu

The impacts of Emission trading on building the optimal bidding strategy for a generation company participating in a day-ahead electricity market is investigated. The CO2 emission price in an emissions trading market is evaluated by using an optimization approach similar to the well-developed probabilistic production simulation method. Then upon the assumption that the probability distribution functions of rivals bidding are known, a stochastic optimization model for building the risk-constrained optimal bidding strategy for the generation company in the framework of the chance-constrained programming is presented. Finally, a numerical example is served for demonstrating the feasibility of the developed model and method, and the optimal bidding results are compared for the two situations with and without the CO2 emissions trading.


2020 ◽  
Vol 10 (20) ◽  
pp. 7310
Author(s):  
Zhaofang Song ◽  
Jing Shi ◽  
Shujian Li ◽  
Zexu Chen ◽  
Wangwang Yang ◽  
...  

As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.


Author(s):  
Arvind Kumar Jain ◽  
S.C. Srivastava

In an electricity market, suppliers are more concerned with maximizing their profit and minimizing the financial risk, which can be achieved through strategic bidding. In this paper, Equal Incremental Cost Criteria (EICC) has been used for developing the optimal bidding strategy. The rival's bidding behavior has been formulated using a stochastic optimization model. Genetic Algorithm (GA), along with ac sensitivity factors, has been used to decide the optimal bidding strategy including congestion management to maximize the profit of the suppliers, considering single sided as well as double sided bidding. Both pure as well as probabilistic strategies have been simulated. Results with Sequential Quadratic Programming (SQP), a classical optimization method, and dc sensitivity factors have also been obtained to compare and establish the effectiveness of proposed method. Value at Risk (VaR) has been calculated as a measure of financial risk.


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